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arXiv · 2503.14049

A Modular Edge Device Network for Surgery Digitalization

Vincent Schorp·Frédéric Giraud·Gianluca Pargätzi·Michael Wäspe·Lorenzo von Ritter-Zahony·Marcel Wegmann·Nicola A. Cavalcanti·John Garcia Henao·Nicholas Bünger·Dominique Cachin·Sebastiano Caprara·Philipp Fürnstahl·Fabio Carrillo
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Abstract

Future surgical care demands real-time, integrated data to drive informed decision-making and improve patient outcomes. The pressing need for seamless and efficient data capture in the OR motivates our development of a modular solution that bridges the gap between emerging machine learning techniques and interventional medicine. We introduce a network of edge devices, called Data Hubs (DHs), that interconnect diverse medical sensors, imaging systems, and robotic tools via optical fiber and a centralized network switch. Built on the NVIDIA Jetson Orin NX, each DH supports multiple interfaces (HDMI, USB-C, Ethernet) and encapsulates device-specific drivers within Docker containers using the Isaac ROS framework and ROS2. A centralized user interface enables straightforward configuration and real-time monitoring, while an Nvidia DGX computer provides state-of-the-art data processing and storage. We validate our approach through an ultrasound-based 3D anatomical reconstruction experiment that combines medical imaging, pose tracking, and RGB-D data acquisition.

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Vincent Schorp, Frédéric Giraud, Gianluca Pargätzi, Michael Wäspe, Lorenzo von Ritter-Zahony, Marcel Wegmann, Nicola A. Cavalcanti, John Garcia Henao, Nicholas Bünger, Dominique Cachin, Sebastiano Caprara, Philipp Fürnstahl, Fabio Carrillo. 2025-03-18. A Modular Edge Device Network for Surgery Digitalization. https://arxiv.org/abs/2503.14049

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